Obtaining artifact-corrected signals in fiber photometry: Isosbestic signals, robust regression and dF/F calculations

Abstract

Abstract Fiber photometry is a powerful tool to measure a wide variety of dynamics from targeted cell populations and circuits in freely-behaving animals. However, measured biosensor signals are contaminated by various artifacts (photobleaching, movement-related, noise) that undermine analysis and interpretation. Here, we consider existing approaches for obtaining artifact-corrected neural dynamic signals from fiber photometry data. We show using real and simulated photometry data that a specific form of robust regression, iteratively reweighted least squares (IRLS), is preferable to ordinary least squares (OLS) regression for fitting isosbestic signals to experimental signals. We also demonstrate the efficacy of low-pass filtering signals and baseline-normalization via dF/F calculations. Considerations and recommendations for analyses, including methods for detrending and normalization are discussed.